An Interaction Model for Simulation and Mitigation of Cascading Failures

In this paper the interactions between component failures are quantified and the interaction matrix and interaction network are obtained. The quantified interactions can capture the general propagation patterns of the cascades from utilities or simulation, thus helping to better understand how cascading failures propagate and to identify key links and key components that are crucial for cascading failure propagation. By utilizing these interactions a high-level probabilistic model called interaction model is proposed to study the influence of interactions on cascading failure risk and to support online decision-making. It is much more time efficient to first quantify the interactions between component failures with fewer original cascades from a more detailed cascading failure model and then perform the interaction model simulation than it is to directly simulate a large number of cascades with a more detailed model. Interaction-based mitigation measures are suggested to mitigate cascading failure risk by weakening key links, which can be achieved in real systems by wide area protection such as blocking of some specific protective relays. The proposed interaction quantifying method and interaction model are validated with line outage data generated by the AC OPA cascading simulations on the IEEE 118-bus system.Comment: Accepted by IEEE Transactions on Power System

Topological analysis of the power grid and mitigation strategies against cascading failures

This paper presents a complex systems overview of a power grid network. In recent years, concerns about the robustness of the power grid have grown because of several cascading outages in different parts of the world. In this paper, cascading effect has been simulated on three different networks, the IEEE 300 bus test system, the IEEE 118 bus test system, and the WSCC 179 bus equivalent model, using the DC Power Flow Model. Power Degradation has been discussed as a measure to estimate the damage to the network, in terms of load loss and node loss. A network generator has been developed to generate graphs with characteristics similar to the IEEE standard networks and the generated graphs are then compared with the standard networks to show the effect of topology in determining the robustness of a power grid. Three mitigation strategies, Homogeneous Load Reduction, Targeted Range-Based Load Reduction, and Use of Distributed Renewable Sources in combination with Islanding, have been suggested. The Homogeneous Load Reduction is the simplest to implement but the Targeted Range-Based Load Reduction is the most effective strategy.Comment: 5 pages, 8 figures, 1 table. This is a limited version of the work due to space limitations of the conference paper. A detailed version is submitted to the IEEE Systems Journal and is currently under revie

Estimating the Propagation of Interdependent Cascading Outages with Multi-Type Branching Processes

In this paper, the multi-type branching process is applied to describe the statistics and interdependencies of line outages, the load shed, and isolated buses. The offspring mean matrix of the multi-type branching process is estimated by the Expectation Maximization (EM) algorithm and can quantify the extent of outage propagation. The joint distribution of two types of outages is estimated by the multi-type branching process via the Lagrange-Good inversion. The proposed model is tested with data generated by the AC OPA cascading simulations on the IEEE 118-bus system. The largest eigenvalues of the offspring mean matrix indicate that the system is closer to criticality when considering the interdependence of different types of outages. Compared with empirically estimating the joint distribution of the total outages, good estimate is obtained by using the multitype branching process with a much smaller number of cascades, thus greatly improving the efficiency. It is shown that the multitype branching process can effectively predict the distribution of the load shed and isolated buses and their conditional largest possible total outages even when there are no data of them.Comment: Accepted by IEEE Transactions on Power System

Quantifying the Influence of Component Failure Probability on Cascading Blackout Risk

The risk of cascading blackouts greatly relies on failure probabilities of individual components in power grids. To quantify how component failure probabilities (CFP) influences blackout risk (BR), this paper proposes a sample-induced semi-analytic approach to characterize the relationship between CFP and BR. To this end, we first give a generic component failure probability function (CoFPF) to describe CFP with varying parameters or forms. Then the exact relationship between BR and CoFPFs is built on the abstract Markov-sequence model of cascading outages. Leveraging a set of samples generated by blackout simulations, we further establish a sample-induced semi-analytic mapping between the unbiased estimation of BR and CoFPFs. Finally, we derive an efficient algorithm that can directly calculate the unbiased estimation of BR when the CoFPFs change. Since no additional simulations are required, the algorithm is computationally scalable and efficient. Numerical experiments well confirm the theory and the algorithm

Modeling, Simulation, and Analysis of Cascading Outages in Power Systems

Interconnected power systems are prone to cascading outages leading to large-area blackouts. Modeling, simulation, analysis, and mitigation of cascading outages are still challenges for power system operators and planners.Firstly, the interaction model and interaction graph proposed by [27] are demonstrated on a realistic Northeastern Power Coordinating Council (NPCC) power system, identifying key links and components that contribute most to the propagation of cascading outages. Then a multi-layer interaction graph for analysis and mitigation of cascading outages is proposed. It provides a practical, comprehensive framework for prediction of outage propagation and decision making on mitigation strategies. It has multiple layers to respectively identify key links and components, which contribute the most to outage propagation. Based on the multi-layer interaction graph, effective mitigation strategies can be further developed. A three-layer interaction graph is constructed and demonstrated on the NPCC power system.Secondly, this thesis proposes a novel steady-state approach for simulating cascading outages. The approach employs a power flow-based model that considers static power-frequency characteristics of both generators and loads. Thus, the system frequency deviation can be calculated under cascading outages and control actions such as under-frequency load shedding can be simulated. Further, a new AC optimal power flow model considering frequency deviation (AC-OPFf) is proposed to simulate remedial control against system collapse. Case studies on the two-area, IEEE 39-bus, and NPCC power systems show that the proposed approach can more accurately capture the propagation of cascading outages when compared with a conventional approach using the conventional power flow and AC optimal power flow models.Thirdly, in order to reduce the potential risk caused by cascading outages, an online strategy of critical component-based active islanding is proposed. It is performed when any component belonging to a predefined set of critical components is involved in the propagation path. The set of critical components whose fail can cause large risk are identified based on the interaction graph. Test results on the NPCC power system show that the cascading outage risk can be reduced significantly by performing the proposed active islanding when compared with the risk of other scenarios without active islanding

Impact Assessment of Hypothesized Cyberattacks on Interconnected Bulk Power Systems

The first-ever Ukraine cyberattack on power grid has proven its devastation by hacking into their critical cyber assets. With administrative privileges accessing substation networks/local control centers, one intelligent way of coordinated cyberattacks is to execute a series of disruptive switching executions on multiple substations using compromised supervisory control and data acquisition (SCADA) systems. These actions can cause significant impacts to an interconnected power grid. Unlike the previous power blackouts, such high-impact initiating events can aggravate operating conditions, initiating instability that may lead to system-wide cascading failure. A systemic evaluation of "nightmare" scenarios is highly desirable for asset owners to manage and prioritize the maintenance and investment in protecting their cyberinfrastructure. This survey paper is a conceptual expansion of real-time monitoring, anomaly detection, impact analyses, and mitigation (RAIM) framework that emphasizes on the resulting impacts, both on steady-state and dynamic aspects of power system stability. Hypothetically, we associate the combinatorial analyses of steady state on substations/components outages and dynamics of the sequential switching orders as part of the permutation. The expanded framework includes (1) critical/noncritical combination verification, (2) cascade confirmation, and (3) combination re-evaluation. This paper ends with a discussion of the open issues for metrics and future design pertaining the impact quantification of cyber-related contingencies

Failure Localization in Power Systems via Tree Partitions

Cascading failures in power systems propagate non-locally, making the control and mitigation of outages extremely hard. In this work, we use the emerging concept of the tree partition of transmission networks to provide an analytical characterization of line failure localizability in transmission systems. Our results rigorously establish the well perceived intuition in power community that failures cannot cross bridges, and reveal a finer-grained concept that encodes more precise information on failure propagations within tree-partition regions. Specifically, when a non-bridge line is tripped, the impact of this failure only propagates within well-defined components, which we refer to as cells, of the tree partition defined by the bridges. In contrast, when a bridge line is tripped, the impact of this failure propagates globally across the network, affecting the power flow on all remaining transmission lines. This characterization suggests that it is possible to improve the system robustness by temporarily switching off certain transmission lines, so as to create more, smaller components in the tree partition; thus spatially localizing line failures and making the grid less vulnerable to large-scale outages. We illustrate this approach using the IEEE 118-bus test system and demonstrate that switching off a negligible portion of transmission lines allows the impact of line failures to be significantly more localized without substantial changes in line congestion